TSCH Multiflow Scheduling with QoS Guarantees: A Comparison of SDN with Common Schedulers

Industrial Wireless Sensor Networks (IWSN) are becoming increasingly popular in production environments due to their ease of deployment, low cost and energy efficiency. However, the complexity and accuracy demanded by these environments requires that IWSN implement quality of service mechanisms that allow them to operate with high determinism. For this reason, the IEEE 802.15.4e standard incorporates the Time Slotted Channel Hopping (TSCH) protocol which reduces interference and increases the reliability of transmissions. This standard does not specify how time resources are allocated in TSCH scheduling, leading to multiple scheduling solutions. Schedulers can be classified as autonomous, distributed and centralised. The first two have prevailed over the centralised ones because they do not require high signalling, along with the advantages of ease of deployment and high performance. However, the increased QoS requirements and the diversity of traffic flows that circulate through the network in today’s Industry 4.0 environment require strict, dynamic control to guarantee parameters such as delay, packet loss and deadline, independently for each flow. That cannot always be achieved with distributed or autonomous schedulers. For this reason, it is necessary to use centralised protocols with a disruptive approach, such as Software Defined Networks (SDN). In these, not only is the control of the MAC layer centralised, but all the decisions of the nodes that make up the network are configured by the controller based on a global vision of the topology and resources, which allows optimal decisions to be made. In this work, a comparative analysis is made through simulation and a testbed of the different schedulers to demonstrate the benefits of a fully centralized approach such as SDN. The results obtained show that with SDN it is possible to simplify the management of multiple flows, without the problems of centralised schedulers. SDN maintains the Packet Delivery Ratio (PDR) levels of other distributed solutions, but in addition, it achieves greater determinism with bounded end-to-end delays and Deadline Satisfaction Ratio (DSR) at the cost of increased power consumption.

A Hybrid Machine Learning Approach for Performance Modeling of Cloud-Based Big Data Applications

Nowadays, Apache Hadoop and Apache Spark are two of the most prominent distributed solutions for processing big data applications on the market. Since in many cases these frameworks are adopted to support business critical activities, it is often important to predict with fair confidence the execution time of submitted applications, for instance when service-level agreements are established with end-users. In this work, we propose and validate a hybrid approach for the performance prediction of big data applications running on clouds, which exploits both analytical modeling and machine learning (ML) techniques and it is able to achieve a good accuracy without too many time consuming and costly experiments on a real setup. The experimental results show how the proposed approach attains improvement in accuracy, number of experiments to be run on the operational system and cost over applying ML techniques without any support from analytical models. Moreover, we compare our approach with Ernest, an ML-based technique proposed in the literature by the Spark inventors. Experiments show that Ernest can accurately estimate the performance in interpolating scenarios while it fails to predict the performance when configurations with increasing number of cores are considered. Finally, a comparison with a similar hybrid approach proposed in the literature demonstrates how our approach significantly reduce prediction errors especially when few experiments on the real system are performed.

A Decomposition-Based Multiobjective Optimization Evolutionary Algorithm with Adaptive Weight Generation Strategy

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is the seminal framework of multiobjective evolutionary algorithms (MOEAs). To alleviate the nonuniformly distributed solutions generated by a fixed set of evenly distributed weight vectors in the presence of nonconvex and disconnected problems, an adaptive vector generation mechanism is proposed. A coevolution strategy and a vector generator are synergistically cooperated to remedy the weight vectors. Optimal weight vectors are generated to replace the useless weight vectors to assure that optimal solutions are distributed evenly. Experiment results indicate that this mechanism is efficient in improving the diversity of MOEA/D.

A New Multiobjective Time-Cost Trade-Off for Scheduling Maintenance Problem in a Series-Parallel System

In this paper, a modified model of Nondominated Sorting Genetic Algorithm 2 (NSGA-II), which is one of the Multiobjective Evolutionary Algorithms, is proposed. This algorithm is a new model designed to make a trade-off between minimizing the cost of preventive maintenance (PM) and minimizing the time taken to perform this maintenance for a series-parallel system. In this model, the limitations of labor and equipment of the maintenance team and the effects of maintenance issues on manufacturing problems are also considered. In the mathematical model, finding the appropriate objective functions for the maintenance scheduling problem requires all maintenance costs and failure rates to be integrated. Additionally, the effects of production interruption during preventive maintenance are added to objective functions. Furthermore, to make a better performance compared with a regular NSGA-II algorithm, we proposed a modified algorithm with a repository to keep more unacceptable solutions. These solutions can be modified and changed with the proposed mutation algorithm to acceptable solutions. In this algorithm, modified operators, such as simulated binary crossover and polynomial mutation, will improve the algorithm to generate convergence and uniformly distributed solutions with more diverse solutions. Finally, by comparing the experimental solutions with the solutions of two Strength Pareto Evolutionary Algorithm 2 (SPEA2) and regular NSGA-II, MNSGA-II generates more efficient and uniform solutions than the other two algorithms.

A Multi-Objective Cloud Workflow Scheduling Optimization Based on Evolutionary Multi-objective Algorithm with Decomposition

In the cloud computing environment, cost-effective workflow task scheduling is the key problem that cloud computing service providers need to solve. However, previous scheduling methods only consider one-sided demands, such as minimizing running time or running cost. In this paper, the cloud workflow scheduling model including two minimizing time and execution cost are established, and then the MOEA/D algorithm based on weight vector adjustment and local search is proposed, and the algorithm is applied in the model solving process. Firstly, the weight vector adjustment method is employed to obtain more evenly distributed solutions; and in order to obtain more evenly distributed solutions and hope to speed up the convergence speed of the solution process, this paper adds local search operators into the solution process of evolutionary algorithm, and proposes MOEA/D algorithm based on local search and weight vector adjustment as an improved multi-objective optimization algorithm to solve the cloud workflow scheduling model based on time and execution cost, it can be turned out that MOEA/D algorithm based on local search and weight vector adjustment can obtain more evenly distributed solutions than MOEA/D algorithm and NSGA-II algorithm on the basis of faster convergence speed, which provides decision support for cloud workflow scheduling decision-makers.

The empathic (r)evolution. Lessons learned from Covid-19 to design at the community, organization, and governmental levels.

The pandemic has revolutionized economic, social, and political models and broken down private and public systems, probably irreversibly. The gap between top-down and bottom-up approaches has widened, favoring divergences between centralized approaches and distributed solutions. The need to rethink rhythms, relationships, places, organizations and governance models emerged, as well as, to rethink the way we create relationships and we design. The paper discusses the adoption of an empathic component in the governance of complex ecosystems to make them more resilient to unexpected phenomena such as Covid-19. The aim is to bring a design perspective discussing the need for an ‘empathic revolution’, namely the adoption of empathy as a lever of innovation for communities, businesses, organizations, and governments. The hypothesis is to adopt empathy not only to understand the users' needs in the development of new products and services, but to extend its adoption also in organizational changes up to transformative processes. In the first part, empathy is described through an extra-disciplinary observation. The second part outlines how empathy has been adopted in the design field. The third part analyzes - through the empathic component - some phenomena that occurred during the pandemic at a community, organizational, and governmental level.

Scaling industrial applications for the big data era

Industrial applications tend to rely increasingly on large datasets for regular operations. In order to facilitate that need, we unite the increasingly available hardware resources with fundamental problems found in classical algorithms. We show solutions to the following problems: power flow and island detection in power networks, and the more general graph sparsification. At their core lie respectively algorithms for solving systems of linear equations, graph connectivity and matrix multiplication, and spectral sparsification of graphs, which are applicable on their own to a far greater spectrum of problems. The novelty of our approach lies in developing the first open source and distributed solutions, capable of handling large datasets. Such solutions constitute a toolkit, which, aside from the initial purpose, can be used for the development of unrelated applications and for educational purposes in the study of distributed algorithms.

Hierarchical Multiobjective Dispatching Strategy for the Microgrid System Using Modified MOEA/D

The large-scale electric vehicles connected to the microgrid have brought various challenges to the safe and economic operation of the microgrid. In this paper, a hierarchical microgrid dispatching strategy considering the user-side demand is proposed. According to the operation characteristics of each dispatch unit, the strategy divides the microgrid system into two levels: source-load level and source-grid-load level. At the source-load level, priority should be given to the use of the renewable energy output. On the basis of considering the user demand, energy storage, electric vehicles, and dispatchable loads should be utilized to maximize the consumption of the renewable energy and minimize the user’s electricity cost. The source-grid-load level can smooth the tie-line power fluctuation through dispatching of the power grid and diesel generators. Furthermore, the study presents a modified MOEA/D algorithm to solve the hierarchical scheduling problem. In the proposed algorithm, a modified Tchebycheff decomposition method is introduced to obtain uniformly distributed solutions. In addition, initialization and replacement strategies are introduced to enhance the convergence and diversity. A wind-photovoltaic-diesel-storage hybrid power system is considered to verify the performance of the proposed dispatching strategy and the modified algorithm. The obtained results are compared with other dispatching approaches, and the comparisons confirm the effectiveness and scientificity of the proposed strategy and algorithm.